PhD Thesis Presentation
Characterisation of Intertidal Vegetation on European Coasts Using MultiScale Remote Sensing in Response to Natural and Anthropogenic Pressures
The 15th of May 2025
Thesis supervisor:
Laurent Barillé, Professor
Co-supervisor:
Pierre Gernez, Lecturer
Jury members:
Antoine Collin
Rodney Forster
Evangelos Spyrakos
Bárbara Ondiviela
Federica Braga
Laurent Barillé
Pierre Gernez
Lecturer
Professor
Professor
Senior scientist
Senior Researcher
Professor
Lecturer
École Pratique des Hautes Études (EPHE), Dinard, France
University of Hull, United Kingdom
University of Stirling, United Kingdom
Universidad de Cantabria, Spain
CNR-ISMAR, Venice, Italy
Nantes Université, France
Nantes Université, France

Simon Oiry



Areas where the land masses meet the seas



Interface regions where land and sea meet
Source: Cosby et al. (2024) , Reimann et al., (2023)
1 billion people (15%) within 10km (4%)
~3 billion by 2100
Hotspots of Economic Growth










Seaport
Dredging
Aquaculture
Energy Production
Artificialisation
The mark of human activity on nature





Oil spills
Erosion
Alien Species Introduction
Climate change
Habitat destruction
Living on the edge of land and sea
Areas between high and low tide





Saltmarshes
Mangroves
Polychaete reefs
Rocky reefs
Tidal flats
Oyster reefs
A rich variety of intertidal habitats
Soft-bottom substrates
Guadalquivir River, Spain
Five Taxonomic Classes
of Vegetation
Hard-bottom substrates
Vigo, Spain





Saja estuary, Spain
Ecosystem Services
~ $30 trillion per year
Protect these ecosystems:


Good knowledge and monitoring to inform policies

A tool to map them all !
Traditional sampling methods:
Remote Sensing:
From the sky to the sea
The science of obtaining information about objects or areas from a distance




Applied to Earth Observation:




From the sky to the sea
Resolution Trade-offs



Sentinel-2
Drone
10–60 m spatial resolution
100 000 km²/image
5-day revisit
cm resolution
Adapted to small-scale studies
Flight planning flexibility
Fieldwork remains essential to make sense of what satellites see
Radiometric calibration
Aven, France

Ground truthing
Noirmoutier, France

Features georeferencing
Tainaron, Greece

Sampling
Cadiz, Spain

Show how remote sensing can effectively map intertidal habitats and assess environmental pressures
Analysing the potential of multispectral sensors to distinguish macrophytes in soft-bottom intertidal zones at low tide
Building an algorithm that discriminates the most common taxonomic classes of vegetation found on soft bottom intertidal sediment
Investigate the capacity of remote sensing to monitor intertidal vegetation under abiotic and biotic pressures
Introduction to Spectroradiometry



\[R(\lambda) = \frac{L_{\text{u}}(\lambda)}{L_{\text{d}}(\lambda)}\]


\[R_i^*(\lambda) = \frac{R_i(\lambda) - \min(R_i)}{\max(R_i) - \min(R_i)}\]















ASD FieldSpec Handheld 2
Hyperspectral Sensor
A lot of Narrow Spectral Bands






Building a Spectral library of intertidal vegetation

Total of 332 Spectra of 5 taxonomic classes
Spectral degradation







ASD
PRISMA
Drone
S2 - 20m
Pléiades
S2 - 10m
250 bands
50 Bands
10 Bands
8 Bands
4 Bands
4 Bands

Spectral comparisons
Compare the Spectra:

Compare the Sensors:

Training of the model:
Validation of the model:
Putting theory into practice

DJI Matrice 200

Micasense RedEdge-MX Dual






Sentinel-2: 100 pixels/hectar
Drone 120 m: ~1 500 000 pixels/hectar
Drone 12 m: ~15 000 000 pixels/hectar
50% Spectralon

Downwelling Light Sensor









Hyperspectral library
Hyperspectral library - nMDS
Hyperspectral library - Random Forest Classifier









Drone imagery - Example of classification


Chlorophyceae

Bacillariophyceae

Magnoliopsida

Florideophyceae


Drone imagery - Validation

Drone imagery - Variable importance


Pigment Composition, Spectral Signature and Variable Importance

Similar pigment composition,…
Distinction between green macrophyte possible using multispectral resolutions, …
Green macrophytes often co-occurs in intertidal areas…
Green macrophytes often co-occurs in intertidal areas…
Drone: 0.26 ha ~ 2.5 millions pixels
S2: 25 000 hectares ~ 2.5x Paris



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Bourgneuf Bay, July 2024
History of the aquaculture of the oyster in Europe

Flat Oyster



Portuguese Oyster

Pacific Oyster



A Hidden Passenger
Resilient to:
Well adapted to European estuaries
Gracilaria vermiculophylla

First observation in Europe in the Belon, Brittany, in 1996
Belon Estuary, France, 2024

Aveiro, Portugal, 2021
Etel, France, 2024

Auray, France, 2024

Scorff, France, 2024

Saja estuary, Spain, 2024

Source: Mendoza-Segura et al., 2023

source: Buestel et al., 2009

Ecological Impacts of the invasion
Negatives:
Positives:
Monitoring and Managing
Remote Sensing as a tool to follow the invasion
Satellite & Aerial views:
Drone:
Make the first description of G. vermiculophylla spatial distribution using remote sensing techniques
Using RS archives to assess historical invasion in the Belon Estuary
Use DISCOV to map G. vermiculophylla and link its spatial distribution to the mudflat topography.
Historical analysis
Sciences et Techniques, Nantes, 1962


Maps and Aerial photographs archives
Photo interpretation of images to retrieve the area covered by G. vermiculophylla


Drone Mapping
DJI Matrice 300
4 Drone flight over G. vermiculophylla

Micasense RedEdge-MX Dual

DJI Zenmuse L1


2 Instruments:
10 Spectral bands between 444 and 840 nm



Digital Surface Model:
Generalised Linear Mixed Model
\[ \begin{align*} \mathrm{Cover}_{ij} &\sim \mathrm{Beta}\bigl(\mu_{ij}\,\phi,\,(1-\mu_{ij})\,\phi\bigr),\\[1em] \mu_{ij} &= \mathrm{logit}(\eta_{ij}), \\[1em] \eta_{ij} &= \underbrace{\alpha_j}_{\substack{\text{intercept for}\\\text{site }j}} + \underbrace{\beta_1\,\mathrm{Bathymetry}_{ij}}_{\text{effect of elevation}} + \underbrace{\beta_2\,\mathrm{Slope}_{ij}}_{\text{effect of slope}}. \end{align*} \]
Historical records in the Belon estuary














Drone flights


Chlorophyceae

Florideophyceae

Saltmarshes

Presence/Absence of Red Algae: 91.1%

Topography of the mudflat
RGB Composition

DSM Color Composition

Slope Categorized

Elevation vs Presence of Algae






First map of the spatial distribution of G. vermiculophylla:
First map of the spatial distribution of G. vermiculophylla
Drone mapping G. vermiculophylla with machine learning

Saja estuary, Spain

Belon estuary, France


Distribution linked with the topography
Distribution linked with the topography


Invasion phases





Lag Phase
Expansion Phase
Saturation Phase
Short Lag phase
Remote Sensing can monitor early stages of the invasion…
Browning of seagrasses across Europe
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Aveiro Lagoon, Portugal, June 2022



Quiberon, France, September 2021



What’s in the litterature ?




On subtidal Zostera marina and Cymodocea nodosa:
What about Zostera noltei ?
Impact on the reflectance ?
Impact of Extreme Atmospheric temperature ?
Extreme Temperature Events = Heatwaves


Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years
Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years
Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years
Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years
Defined as periods of three or more consecutive days where the daily maximum temperature (Tmax) exceeds the calendar-day 90th percentile based on at least 30 years




Hypothesis & Objectives
Heatwaves alter the spectral reflectance of Zostera noltei seagrass. This change can be detected using remote sensing.
Experiment in the Lab
Intertidal chambers from ElectricBlue



Experiment Tank
Storage Tank

Measure variation of seagrass leaves reflectance over time.

Seagrasses inside of a chamber



Hyperspectral measurement every minute in each tank
Control
Treatment


Satellite Mapping of the impact of Heatwaves on seagrasses
Atmospheric heatwave between the 4th of September 2021 and the 7th of September 2021 in Quiberon




3 Sentinel-2 images, level L2A, Low Tide:
3 Sentinel-2 images, level L2A, Low Tide:
Before: 1st of September 2021
During: 6th of September 2021
3 Sentinel-2 images, level L2A, Low Tide:
Before: 1st of September 2021
During: 6th of September 2021
After: 8th of October 2021
detailed 3D coastal and nearshore mapping



Both Experimental and Satellite Mapping
\[ f''(\lambda_i) \approx \frac{f(\lambda_{i+1}) - 2f(\lambda_i) + f(\lambda_{i-1})}{(\Delta \lambda)^2} \]
\[NDVI = \frac{R(NIR)-R(Red)}{R(NIR)+R(Red)}\]
\[GLI = \frac{[R(Green)-R(Red)]+[R(Green)-R(Blue)]}{(2 \times R(Green) )+ R(Red) + R(Blue) }\]
Spectral signatures:
SHSI design



Heatwave experiment
Spectral metrics:





\(R''_{665 \, \text{nm}}\) drops by 68 %
\(NDVI\) drops by 31 %
\(GLI\) drops by 54 %
\(SHSI\) increases by 420 %
in situ Satellite Mapping


Before
During





Before
During
After

Mapping Impacted meadows
Seagrasses impacted by heatwave have a distinct spectral signature

Thermal stress = Oxydative stress
Oxydative stress = Membrane damages
Possible to detect seagrass thermal stress using satellite remote sensing, using SHSI






Satellite mapping reveals tide-modulated, patchy impacts


Rapid global escalation of HW frequency, intensity and duration
Key Success: Multispectral RS + ML effectively differentiate intertidal vegetation, even with similar pigments (e.g., seagrass vs. green macroalgae).
Core Mechanism: Relies on detecting subtle spectral variations (pigment proportions & concentrations).
DISCOV Algorithm – A Key Output:


Ongoing Challenge: Natural variability in pigment content (phenology, stress, intraspecies variation).
Future Direction: Continue expanding the training dataset (broader geographic/temporal range) to enhance algorithm generalisability.
Significance of UAVs (Drones):
Applications Demonstrated in Associated Research:
Aquaculture Monitoring: Estimating Kappaphycus alvarezii biomass & carrageenan (Nurdin et al., 2023).

Applications Demonstrated in Associated Research:
Oyster Farm Assessment: Mapping intertidal oyster farm structures, mesh bag sizes, & table heights (A. Román et al., 2023).





Complementary Strengths:
Example: The ICE CREAMS Model (Davies et al., 2024a,b):
Methodology: DISCOV (drone-derived) classifications used to train and validate ICE CREAMS (Sentinel-2 satellite) model.
Achievements:
Key Benefit: This synergistic approach balances local accuracy with regional/global scalability, optimising monitoring efforts.
RS for Comprehensive Threat Monitoring: Addresses impacts of climate change (heatwaves), eutrophication, invasive species, and habitat fragmentation.
Key Tools & Techniques Used/Discussed:




Broader Application: RS data can be integrated into risk assessment frameworks (e.g., DAPSI(W)R(M)).
Outcome: Provides a holistic understanding of coastal ecosystem dynamics, crucial for informed management and conservation.
Critical Context: Widespread global decline of seagrass meadows due to anthropogenic pressures necessitates urgent and effective restoration efforts.
Restoration Challenges: Success is variable (e.g., Arcachon Bay - Laurent, 2024; initial survival, then significant mortality).
How Remote Sensing Can Revolutionize Restoration:
DISCOV Model’s Role in Seagrass Restoration:
Upcoming Application: EU Project REBORN (Interreg Program):
DISCOV and ICE CREAMS will be applied to:
Thank you for your attention.